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Attention-Augmented LSTM Feed-Forward Compensation for Lever-Arm-Induced Velocity Errors in Transfer Alignment.

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Summary

This study introduces an adaptive Kalman filter (AKF) with a Long Short-Term Memory (LSTM) module to improve underwater robotic navigation accuracy. The method effectively compensates for velocity errors caused by changing lever arms in flexible systems.

Keywords:
AKFAttention-LSTMinertial navigation systemlever-arm effecttransfer alignmentunderwater bio-inspired robot

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Area of Science:

  • Robotics and Control Systems
  • Navigation and Guidance
  • Bio-inspired Systems

Background:

  • Underwater robotic systems face navigation challenges due to time-varying lever arms caused by structural flexure and attitude changes.
  • Traditional rigid body compensation methods fail with flexible components, leading to velocity errors and degraded transfer alignment accuracy.
  • Accurate navigation is crucial for the successful deployment and operation of mother-child underwater robotic systems.

Purpose of the Study:

  • To develop a robust transfer alignment method for underwater robotic systems with time-varying lever arms.
  • To enhance the accuracy and filter convergence of inertial navigation systems (INSs) in flexible robotic platforms.
  • To mitigate systematic velocity errors introduced by structural deformations and attitude variations.

Main Methods:

  • Augmenting a velocity-attitude joint matching and innovation-based adaptive Kalman filter (AKF) with an attention-based Long Short-Term Memory (LSTM) feed-forward module.
  • Utilizing a short, real-time Inertial Measurement Unit (IMU) sequence from the slave INS to predict and compensate velocity bias.
  • Implementing numerical simulations for an underwater bio-inspired robot deployment scenario to validate the proposed method.

Main Results:

  • The proposed method significantly reduced the root-mean-square (RMS) misalignment angle error from approximately 14.5' to 5.2'.
  • RMS installation error angle was reduced from 8.8' to 3.0', representing average reductions of about 64% and 66%, respectively.
  • Demonstrated substantial improvement in the robustness and practical applicability of transfer alignment under dynamic conditions.

Conclusions:

  • The LSTM-augmented AKF framework effectively compensates for velocity bias induced by time-varying lever arms in flexible underwater robotic systems.
  • The enhanced transfer alignment method improves navigation accuracy and filter convergence, outperforming traditional rigid body compensation.
  • The approach offers improved robustness and practical applicability for complex underwater robotic missions.